Overview

Dataset statistics

Number of variables21
Number of observations51707
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 MiB
Average record size in memory140.0 B

Variable types

Numeric12
Categorical5
Boolean4

Alerts

cleanliness_rating is highly overall correlated with guest_satisfaction_overallHigh correlation
guest_satisfaction_overall is highly overall correlated with cleanliness_ratingHigh correlation
dist is highly overall correlated with rest_index_normHigh correlation
attr_index is highly overall correlated with attr_index_norm and 2 other fieldsHigh correlation
attr_index_norm is highly overall correlated with attr_index and 3 other fieldsHigh correlation
rest_index is highly overall correlated with attr_index and 2 other fieldsHigh correlation
rest_index_norm is highly overall correlated with dist and 3 other fieldsHigh correlation
lng is highly overall correlated with cityHigh correlation
lat is highly overall correlated with attr_index_norm and 1 other fieldsHigh correlation
room_type is highly overall correlated with room_shared and 1 other fieldsHigh correlation
room_shared is highly overall correlated with room_typeHigh correlation
room_private is highly overall correlated with room_type and 1 other fieldsHigh correlation
person_capacity is highly overall correlated with room_privateHigh correlation
city is highly overall correlated with lng and 1 other fieldsHigh correlation
room_shared is highly imbalanced (93.9%)Imbalance
realSum is highly skewed (γ1 = 21.41995656)Skewed
dist has unique valuesUnique
metro_dist has unique valuesUnique
attr_index has unique valuesUnique
rest_index has unique valuesUnique
bedrooms has 4485 (8.7%) zerosZeros

Reproduction

Analysis started2023-08-12 13:50:11.004005
Analysis finished2023-08-12 13:50:45.285073
Duration34.28 seconds
Software versionydata-profiling vv4.5.0
Download configurationconfig.json

Variables

realSum
Real number (ℝ)

SKEWED 

Distinct10497
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean279.87959
Minimum34.779339
Maximum18545.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size404.1 KiB
2023-08-12T15:50:45.481834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.779339
5-th percentile96.079488
Q1148.75217
median211.34309
Q3319.69429
95-th percentile661.98641
Maximum18545.45
Range18510.671
Interquartile range (IQR)170.94211

Descriptive statistics

Standard deviation327.94839
Coefficient of variation (CV)1.1717481
Kurtosis831.35586
Mean279.87959
Median Absolute Deviation (MAD)75.403862
Skewness21.419957
Sum14471734
Variance107550.14
MonotonicityNot monotonic
2023-08-12T15:50:45.767098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
184.4621607 188
 
0.4%
161.5505108 167
 
0.3%
126.7154513 162
 
0.3%
115.9984065 145
 
0.3%
138.9637476 133
 
0.3%
104.2813957 128
 
0.2%
207.6076029 128
 
0.2%
149.8608936 127
 
0.2%
103.8038015 126
 
0.2%
230.5192528 116
 
0.2%
Other values (10487) 50287
97.3%
ValueCountFrequency (%)
34.77933919 1
 
< 0.1%
37.12929454 1
 
< 0.1%
39.00925882 1
 
< 0.1%
40.1842365 1
 
< 0.1%
42.88425937 5
< 0.1%
44.1791606 1
 
< 0.1%
45.22766152 8
< 0.1%
46.05709209 3
 
< 0.1%
46.16502238 2
 
< 0.1%
46.39936259 3
 
< 0.1%
ValueCountFrequency (%)
18545.45028 1
< 0.1%
16445.61469 1
< 0.1%
15499.89416 1
< 0.1%
13664.30592 1
< 0.1%
13656.35883 1
< 0.1%
12942.99138 1
< 0.1%
12937.2751 1
< 0.1%
12929.51386 1
< 0.1%
12886.23909 2
< 0.1%
12076.95383 1
< 0.1%

room_type
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size404.1 KiB
Entire home/apt
32648 
Private room
18693 
Shared room
 
366

Length

Max length15
Median length15
Mean length13.887133
Min length11

Characters and Unicode

Total characters718062
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowPrivate room
3rd rowPrivate room
4th rowPrivate room
5th rowPrivate room

Common Values

ValueCountFrequency (%)
Entire home/apt 32648
63.1%
Private room 18693
36.2%
Shared room 366
 
0.7%

Length

2023-08-12T15:50:46.034623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-12T15:50:46.261656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
entire 32648
31.6%
home/apt 32648
31.6%
room 19059
18.4%
private 18693
18.1%
shared 366
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 84355
11.7%
t 83989
11.7%
o 70766
9.9%
r 70766
9.9%
a 51707
 
7.2%
51707
 
7.2%
m 51707
 
7.2%
i 51341
 
7.1%
h 33014
 
4.6%
p 32648
 
4.5%
Other values (7) 136062
18.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 582000
81.1%
Space Separator 51707
 
7.2%
Uppercase Letter 51707
 
7.2%
Other Punctuation 32648
 
4.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 84355
14.5%
t 83989
14.4%
o 70766
12.2%
r 70766
12.2%
a 51707
8.9%
m 51707
8.9%
i 51341
8.8%
h 33014
 
5.7%
p 32648
 
5.6%
n 32648
 
5.6%
Other values (2) 19059
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
E 32648
63.1%
P 18693
36.2%
S 366
 
0.7%
Space Separator
ValueCountFrequency (%)
51707
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 32648
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 633707
88.3%
Common 84355
 
11.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 84355
13.3%
t 83989
13.3%
o 70766
11.2%
r 70766
11.2%
a 51707
8.2%
m 51707
8.2%
i 51341
8.1%
h 33014
 
5.2%
p 32648
 
5.2%
E 32648
 
5.2%
Other values (5) 70766
11.2%
Common
ValueCountFrequency (%)
51707
61.3%
/ 32648
38.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 718062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 84355
11.7%
t 83989
11.7%
o 70766
9.9%
r 70766
9.9%
a 51707
 
7.2%
51707
 
7.2%
m 51707
 
7.2%
i 51341
 
7.1%
h 33014
 
4.6%
p 32648
 
4.5%
Other values (7) 136062
18.9%

room_shared
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
False
51341 
True
 
366
ValueCountFrequency (%)
False 51341
99.3%
True 366
 
0.7%
2023-08-12T15:50:46.423463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

room_private
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
False
33014 
True
18693 
ValueCountFrequency (%)
False 33014
63.8%
True 18693
36.2%
2023-08-12T15:50:46.556711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

person_capacity
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size404.1 KiB
2.0
24333 
4.0
14000 
3.0
6165 
6.0
4274 
5.0
2935 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters155121
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row2.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 24333
47.1%
4.0 14000
27.1%
3.0 6165
 
11.9%
6.0 4274
 
8.3%
5.0 2935
 
5.7%

Length

2023-08-12T15:50:46.728637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-12T15:50:46.913588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 24333
47.1%
4.0 14000
27.1%
3.0 6165
 
11.9%
6.0 4274
 
8.3%
5.0 2935
 
5.7%

Most occurring characters

ValueCountFrequency (%)
. 51707
33.3%
0 51707
33.3%
2 24333
15.7%
4 14000
 
9.0%
3 6165
 
4.0%
6 4274
 
2.8%
5 2935
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 103414
66.7%
Other Punctuation 51707
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 51707
50.0%
2 24333
23.5%
4 14000
 
13.5%
3 6165
 
6.0%
6 4274
 
4.1%
5 2935
 
2.8%
Other Punctuation
ValueCountFrequency (%)
. 51707
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 155121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 51707
33.3%
0 51707
33.3%
2 24333
15.7%
4 14000
 
9.0%
3 6165
 
4.0%
6 4274
 
2.8%
5 2935
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 155121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 51707
33.3%
0 51707
33.3%
2 24333
15.7%
4 14000
 
9.0%
3 6165
 
4.0%
6 4274
 
2.8%
5 2935
 
1.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
False
38475 
True
13232 
ValueCountFrequency (%)
False 38475
74.4%
True 13232
 
25.6%
2023-08-12T15:50:47.080801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

multi
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size404.1 KiB
0
36642 
1
15065 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters51707
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 36642
70.9%
1 15065
29.1%

Length

2023-08-12T15:50:47.245409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-12T15:50:47.404669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 36642
70.9%
1 15065
29.1%

Most occurring characters

ValueCountFrequency (%)
0 36642
70.9%
1 15065
29.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51707
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36642
70.9%
1 15065
29.1%

Most occurring scripts

ValueCountFrequency (%)
Common 51707
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36642
70.9%
1 15065
29.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51707
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36642
70.9%
1 15065
29.1%

biz
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size404.1 KiB
0
33599 
1
18108 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters51707
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 33599
65.0%
1 18108
35.0%

Length

2023-08-12T15:50:47.572962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-12T15:50:47.728356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 33599
65.0%
1 18108
35.0%

Most occurring characters

ValueCountFrequency (%)
0 33599
65.0%
1 18108
35.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51707
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 33599
65.0%
1 18108
35.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51707
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 33599
65.0%
1 18108
35.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51707
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33599
65.0%
1 18108
35.0%

cleanliness_rating
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3906241
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size404.1 KiB
2023-08-12T15:50:47.884506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q19
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.95486846
Coefficient of variation (CV)0.10168317
Kurtosis13.611255
Mean9.3906241
Median Absolute Deviation (MAD)0
Skewness-2.8502863
Sum485561
Variance0.91177377
MonotonicityNot monotonic
2023-08-12T15:50:48.078089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
10 30067
58.1%
9 15458
29.9%
8 4352
 
8.4%
7 947
 
1.8%
6 501
 
1.0%
4 143
 
0.3%
2 143
 
0.3%
5 86
 
0.2%
3 10
 
< 0.1%
ValueCountFrequency (%)
2 143
 
0.3%
3 10
 
< 0.1%
4 143
 
0.3%
5 86
 
0.2%
6 501
 
1.0%
7 947
 
1.8%
8 4352
 
8.4%
9 15458
29.9%
10 30067
58.1%
ValueCountFrequency (%)
10 30067
58.1%
9 15458
29.9%
8 4352
 
8.4%
7 947
 
1.8%
6 501
 
1.0%
5 86
 
0.2%
4 143
 
0.3%
3 10
 
< 0.1%
2 143
 
0.3%

guest_satisfaction_overall
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.628232
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size404.1 KiB
2023-08-12T15:50:48.312930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile79
Q190
median95
Q399
95-th percentile100
Maximum100
Range80
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.9455308
Coefficient of variation (CV)0.096574561
Kurtosis17.022341
Mean92.628232
Median Absolute Deviation (MAD)4
Skewness-3.1749078
Sum4789528
Variance80.022522
MonotonicityNot monotonic
2023-08-12T15:50:48.568111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 10681
20.7%
98 3889
 
7.5%
97 3626
 
7.0%
96 3532
 
6.8%
93 3329
 
6.4%
95 3299
 
6.4%
94 2615
 
5.1%
90 2607
 
5.0%
99 2341
 
4.5%
80 2059
 
4.0%
Other values (43) 13729
26.6%
ValueCountFrequency (%)
20 155
0.3%
30 8
 
< 0.1%
40 123
0.2%
44 2
 
< 0.1%
46 1
 
< 0.1%
47 16
 
< 0.1%
50 44
 
0.1%
53 14
 
< 0.1%
54 1
 
< 0.1%
55 4
 
< 0.1%
ValueCountFrequency (%)
100 10681
20.7%
99 2341
 
4.5%
98 3889
 
7.5%
97 3626
 
7.0%
96 3532
 
6.8%
95 3299
 
6.4%
94 2615
 
5.1%
93 3329
 
6.4%
92 2025
 
3.9%
91 1799
 
3.5%

bedrooms
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1587599
Minimum0
Maximum10
Zeros4485
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size404.1 KiB
2023-08-12T15:50:48.781786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.62741029
Coefficient of variation (CV)0.54144976
Kurtosis8.692778
Mean1.1587599
Median Absolute Deviation (MAD)0
Skewness1.3677757
Sum59916
Variance0.39364367
MonotonicityNot monotonic
2023-08-12T15:50:48.969524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 36333
70.3%
2 9290
 
18.0%
0 4485
 
8.7%
3 1477
 
2.9%
4 96
 
0.2%
5 10
 
< 0.1%
9 10
 
< 0.1%
6 2
 
< 0.1%
8 2
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
0 4485
 
8.7%
1 36333
70.3%
2 9290
 
18.0%
3 1477
 
2.9%
4 96
 
0.2%
5 10
 
< 0.1%
6 2
 
< 0.1%
8 2
 
< 0.1%
9 10
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
10 2
 
< 0.1%
9 10
 
< 0.1%
8 2
 
< 0.1%
6 2
 
< 0.1%
5 10
 
< 0.1%
4 96
 
0.2%
3 1477
 
2.9%
2 9290
 
18.0%
1 36333
70.3%
0 4485
 
8.7%

dist
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct51707
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1912851
Minimum0.015044521
Maximum25.284557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size404.1 KiB
2023-08-12T15:50:49.187166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.015044521
5-th percentile0.53091454
Q11.4531417
median2.6135377
Q34.2630771
95-th percentile7.7443212
Maximum25.284557
Range25.269512
Interquartile range (IQR)2.8099354

Descriptive statistics

Standard deviation2.3938026
Coefficient of variation (CV)0.75010613
Kurtosis4.969597
Mean3.1912851
Median Absolute Deviation (MAD)1.3424615
Skewness1.7311945
Sum165011.78
Variance5.7302907
MonotonicityNot monotonic
2023-08-12T15:50:49.429895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.022963798 1
 
< 0.1%
0.9267457613 1
 
< 0.1%
0.9492443402 1
 
< 0.1%
1.810055679 1
 
< 0.1%
2.240424781 1
 
< 0.1%
2.953784515 1
 
< 0.1%
1.661411982 1
 
< 0.1%
1.626053492 1
 
< 0.1%
3.185684333 1
 
< 0.1%
0.3922062454 1
 
< 0.1%
Other values (51697) 51697
> 99.9%
ValueCountFrequency (%)
0.0150445207 1
< 0.1%
0.01505879807 1
< 0.1%
0.03466063682 1
< 0.1%
0.03981359742 1
< 0.1%
0.04055333537 1
< 0.1%
0.04056058641 1
< 0.1%
0.04278870764 1
< 0.1%
0.04331472743 1
< 0.1%
0.04333710768 1
< 0.1%
0.05129393071 1
< 0.1%
ValueCountFrequency (%)
25.28455675 1
< 0.1%
22.61745814 1
< 0.1%
22.61745145 1
< 0.1%
22.59511526 1
< 0.1%
21.29517392 1
< 0.1%
21.29515096 1
< 0.1%
20.89510207 1
< 0.1%
20.89509463 1
< 0.1%
20.49567803 1
< 0.1%
20.49565772 1
< 0.1%

metro_dist
Real number (ℝ)

UNIQUE 

Distinct51707
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.68153984
Minimum0.002301068
Maximum14.273577
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size404.1 KiB
2023-08-12T15:50:49.656257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.002301068
5-th percentile0.11010151
Q10.24847951
median0.41326924
Q30.73784008
95-th percentile2.1243154
Maximum14.273577
Range14.271276
Interquartile range (IQR)0.48936057

Descriptive statistics

Standard deviation0.85802283
Coefficient of variation (CV)1.2589474
Kurtosis23.703047
Mean0.68153984
Median Absolute Deviation (MAD)0.20315805
Skewness4.0604449
Sum35240.38
Variance0.73620318
MonotonicityNot monotonic
2023-08-12T15:50:49.899876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.539380003 1
 
< 0.1%
0.3365377577 1
 
< 0.1%
0.1202082532 1
 
< 0.1%
0.46755128 1
 
< 0.1%
0.2986401695 1
 
< 0.1%
0.3943181888 1
 
< 0.1%
0.3021673129 1
 
< 0.1%
0.3701270869 1
 
< 0.1%
0.5588153189 1
 
< 0.1%
0.2730923921 1
 
< 0.1%
Other values (51697) 51697
> 99.9%
ValueCountFrequency (%)
0.002301068012 1
< 0.1%
0.003220007615 1
< 0.1%
0.003935058041 1
< 0.1%
0.00394375911 1
< 0.1%
0.004750441048 1
< 0.1%
0.004762350151 1
< 0.1%
0.006157628218 1
< 0.1%
0.006170744841 1
< 0.1%
0.006388847148 1
< 0.1%
0.006405009016 1
< 0.1%
ValueCountFrequency (%)
14.27357693 1
< 0.1%
13.31411503 1
< 0.1%
13.31410827 1
< 0.1%
13.0699635 1
< 0.1%
11.68773401 1
< 0.1%
9.598773284 1
< 0.1%
9.573733182 1
< 0.1%
9.286229385 1
< 0.1%
9.174093892 1
< 0.1%
9.151204873 1
< 0.1%

attr_index
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct51707
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean294.2041
Minimum15.152201
Maximum4513.5635
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size404.1 KiB
2023-08-12T15:50:50.145578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15.152201
5-th percentile66.675024
Q1136.79738
median234.33175
Q3385.75638
95-th percentile728.55276
Maximum4513.5635
Range4498.4113
Interquartile range (IQR)248.959

Descriptive statistics

Standard deviation224.75412
Coefficient of variation (CV)0.76393945
Kurtosis23.037393
Mean294.2041
Median Absolute Deviation (MAD)114.23113
Skewness2.7599805
Sum15212412
Variance50514.416
MonotonicityNot monotonic
2023-08-12T15:50:50.589036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.69037927 1
 
< 0.1%
124.6214213 1
 
< 0.1%
373.4523724 1
 
< 0.1%
141.9817664 1
 
< 0.1%
102.1038061 1
 
< 0.1%
53.92603171 1
 
< 0.1%
123.8424983 1
 
< 0.1%
163.2112525 1
 
< 0.1%
50.18719008 1
 
< 0.1%
231.4391473 1
 
< 0.1%
Other values (51697) 51697
> 99.9%
ValueCountFrequency (%)
15.15220147 1
< 0.1%
15.53291806 1
< 0.1%
16.58197413 1
< 0.1%
16.60073055 1
< 0.1%
16.60073515 1
< 0.1%
19.01913316 1
< 0.1%
19.01914767 1
< 0.1%
19.1378197 1
< 0.1%
19.13782442 1
< 0.1%
19.21394433 1
< 0.1%
ValueCountFrequency (%)
4513.563486 1
< 0.1%
4512.59517 1
< 0.1%
4512.345962 1
< 0.1%
4510.73735 1
< 0.1%
4510.436033 1
< 0.1%
4509.914049 1
< 0.1%
4022.618126 1
< 0.1%
3031.840298 1
< 0.1%
3028.991664 1
< 0.1%
2934.133441 1
< 0.1%

attr_index_norm
Real number (ℝ)

HIGH CORRELATION 

Distinct51688
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.423792
Minimum0.92630092
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size404.1 KiB
2023-08-12T15:50:50.822491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.92630092
5-th percentile3.0569712
Q16.3809264
median11.468305
Q317.415082
95-th percentile31.136879
Maximum100
Range99.073699
Interquartile range (IQR)11.034155

Descriptive statistics

Standard deviation9.8079845
Coefficient of variation (CV)0.73064186
Kurtosis9.351918
Mean13.423792
Median Absolute Deviation (MAD)5.416192
Skewness2.1931671
Sum694104.03
Variance96.19656
MonotonicityNot monotonic
2023-08-12T15:50:51.068754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 20
 
< 0.1%
4.166707868 1
 
< 0.1%
5.020137412 1
 
< 0.1%
3.849737769 1
 
< 0.1%
2.033235479 1
 
< 0.1%
4.669376798 1
 
< 0.1%
6.153742424 1
 
< 0.1%
1.892265613 1
 
< 0.1%
8.72621757 1
 
< 0.1%
7.991073781 1
 
< 0.1%
Other values (51678) 51678
99.9%
ValueCountFrequency (%)
0.9263009179 1
< 0.1%
1.040227906 1
< 0.1%
1.040956301 1
< 0.1%
1.134200311 1
< 0.1%
1.135137404 1
< 0.1%
1.141278148 1
< 0.1%
1.142220498 1
< 0.1%
1.145817817 1
< 0.1%
1.147309596 1
< 0.1%
1.148114127 1
< 0.1%
ValueCountFrequency (%)
100 20
< 0.1%
99.9944775 1
 
< 0.1%
99.95215309 1
 
< 0.1%
99.93738572 1
 
< 0.1%
99.91914511 1
 
< 0.1%
99.27831239 1
 
< 0.1%
98.97561989 1
 
< 0.1%
98.97243581 1
 
< 0.1%
98.86827875 1
 
< 0.1%
98.54126635 1
 
< 0.1%

rest_index
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct51707
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean626.8567
Minimum19.576924
Maximum6696.1568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size404.1 KiB
2023-08-12T15:50:51.299334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19.576924
5-th percentile93.464041
Q1250.85411
median522.05278
Q3832.62899
95-th percentile1615.0761
Maximum6696.1568
Range6676.5798
Interquartile range (IQR)581.77487

Descriptive statistics

Standard deviation497.92023
Coefficient of variation (CV)0.79431269
Kurtosis4.7512364
Mean626.8567
Median Absolute Deviation (MAD)287.24938
Skewness1.6943564
Sum32412879
Variance247924.55
MonotonicityNot monotonic
2023-08-12T15:50:51.535290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.25389587 1
 
< 0.1%
216.20763 1
 
< 0.1%
631.5788783 1
 
< 0.1%
206.1115944 1
 
< 0.1%
130.0507776 1
 
< 0.1%
80.03435522 1
 
< 0.1%
226.6451046 1
 
< 0.1%
156.5323018 1
 
< 0.1%
71.58261456 1
 
< 0.1%
381.7061375 1
 
< 0.1%
Other values (51697) 51697
> 99.9%
ValueCountFrequency (%)
19.5769238 1
< 0.1%
21.45579724 1
< 0.1%
21.45580338 1
< 0.1%
21.49691927 1
< 0.1%
25.02270288 1
< 0.1%
26.50937148 1
< 0.1%
26.72904038 1
< 0.1%
27.90161264 1
< 0.1%
27.90167346 1
< 0.1%
27.93416764 1
< 0.1%
ValueCountFrequency (%)
6696.156772 1
< 0.1%
5587.136047 1
< 0.1%
5584.77184 1
< 0.1%
4592.883342 1
< 0.1%
4591.339847 1
< 0.1%
4590.349641 1
< 0.1%
4590.306687 1
< 0.1%
4589.772131 1
< 0.1%
4589.32312 1
< 0.1%
4552.357526 1
< 0.1%

rest_index_norm
Real number (ℝ)

HIGH CORRELATION 

Distinct51688
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.786177
Minimum0.59275692
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size404.1 KiB
2023-08-12T15:50:51.779378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.59275692
5-th percentile2.7221578
Q18.75148
median17.542238
Q332.964603
95-th percentile59.117979
Maximum100
Range99.407243
Interquartile range (IQR)24.213123

Descriptive statistics

Standard deviation17.804096
Coefficient of variation (CV)0.78135513
Kurtosis0.78289621
Mean22.786177
Median Absolute Deviation (MAD)10.614318
Skewness1.0974547
Sum1178204.9
Variance316.98585
MonotonicityNot monotonic
2023-08-12T15:50:52.024694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 20
 
< 0.1%
6.846472824 1
 
< 0.1%
12.29477012 1
 
< 0.1%
9.765003832 1
 
< 0.1%
6.009466453 1
 
< 0.1%
17.01789375 1
 
< 0.1%
11.75339783 1
 
< 0.1%
5.37485833 1
 
< 0.1%
28.66081974 1
 
< 0.1%
36.99739056 1
 
< 0.1%
Other values (51678) 51678
99.9%
ValueCountFrequency (%)
0.5927569191 1
< 0.1%
0.6407212807 1
< 0.1%
0.6460305932 1
< 0.1%
0.6549731588 1
< 0.1%
0.6608714797 1
< 0.1%
0.6659262496 1
< 0.1%
0.6670097117 1
< 0.1%
0.6677877225 1
< 0.1%
0.6743712124 1
< 0.1%
0.6746584383 1
< 0.1%
ValueCountFrequency (%)
100 20
< 0.1%
99.96639378 1
 
< 0.1%
99.94483419 1
 
< 0.1%
99.94389898 1
 
< 0.1%
99.92248394 1
 
< 0.1%
99.19076175 1
 
< 0.1%
98.48673907 1
 
< 0.1%
98.30841086 1
 
< 0.1%
98.11497172 1
 
< 0.1%
98.05204 1
 
< 0.1%

lng
Real number (ℝ)

HIGH CORRELATION 

Distinct23600
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4260676
Minimum-9.22634
Maximum23.78602
Zeros0
Zeros (%)0.0%
Negative15354
Negative (%)29.7%
Memory size404.1 KiB
2023-08-12T15:50:52.267311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9.22634
5-th percentile-9.14439
Q1-0.0725
median4.873
Q313.518825
95-th percentile23.73003
Maximum23.78602
Range33.01236
Interquartile range (IQR)13.591325

Descriptive statistics

Standard deviation9.7997246
Coefficient of variation (CV)1.3196385
Kurtosis-1.0033876
Mean7.4260676
Median Absolute Deviation (MAD)7.605
Skewness0.033300662
Sum383979.68
Variance96.034602
MonotonicityNot monotonic
2023-08-12T15:50:52.510022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.472 98
 
0.2%
-9.133 83
 
0.2%
12.47 78
 
0.2%
12.469 76
 
0.1%
12.468 75
 
0.1%
12.477 71
 
0.1%
12.467 64
 
0.1%
23.729 63
 
0.1%
-9.144 61
 
0.1%
-9.148 59
 
0.1%
Other values (23590) 50979
98.6%
ValueCountFrequency (%)
-9.22634 2
< 0.1%
-9.22599 1
< 0.1%
-9.22594 2
< 0.1%
-9.22516 1
< 0.1%
-9.22476 2
< 0.1%
-9.2219 2
< 0.1%
-9.2211 2
< 0.1%
-9.21994 1
< 0.1%
-9.21973 1
< 0.1%
-9.21947 1
< 0.1%
ValueCountFrequency (%)
23.78602 2
< 0.1%
23.77931 2
< 0.1%
23.77891 2
< 0.1%
23.77889 1
 
< 0.1%
23.77812 1
 
< 0.1%
23.778 2
< 0.1%
23.77702 2
< 0.1%
23.77682 2
< 0.1%
23.776 3
< 0.1%
23.775 2
< 0.1%

lat
Real number (ℝ)

HIGH CORRELATION 

Distinct21484
Distinct (%)41.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.671128
Minimum37.953
Maximum52.64141
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size404.1 KiB
2023-08-12T15:50:52.745827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.953
5-th percentile37.97893
Q141.39951
median47.50669
Q351.471885
95-th percentile52.39158
Maximum52.64141
Range14.68841
Interquartile range (IQR)10.072375

Descriptive statistics

Standard deviation5.2492632
Coefficient of variation (CV)0.11493614
Kurtosis-1.554394
Mean45.671128
Median Absolute Deviation (MAD)4.86972
Skewness-0.17958308
Sum2361517
Variance27.554764
MonotonicityNot monotonic
2023-08-12T15:50:52.985984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.711 136
 
0.3%
41.896 126
 
0.2%
41.897 118
 
0.2%
38.712 116
 
0.2%
41.899 103
 
0.2%
38.71 99
 
0.2%
41.895 93
 
0.2%
41.908 92
 
0.2%
38.713 85
 
0.2%
41.907 83
 
0.2%
Other values (21474) 50656
98.0%
ValueCountFrequency (%)
37.953 2
< 0.1%
37.95302 2
< 0.1%
37.95368 2
< 0.1%
37.9537 2
< 0.1%
37.954 3
< 0.1%
37.95463 1
 
< 0.1%
37.95466 1
 
< 0.1%
37.95482 2
< 0.1%
37.955 2
< 0.1%
37.95514 1
 
< 0.1%
ValueCountFrequency (%)
52.64141 2
< 0.1%
52.63967 2
< 0.1%
52.63429 1
< 0.1%
52.63311 1
< 0.1%
52.6208 2
< 0.1%
52.61913 1
< 0.1%
52.61597 2
< 0.1%
52.61588 2
< 0.1%
52.60997 2
< 0.1%
52.60922 2
< 0.1%

city
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size404.1 KiB
london
9993 
rome
9027 
paris
6688 
lisbon
5763 
athens
5280 
Other values (5)
14956 

Length

Max length9
Median length6
Mean length5.9621134
Min length4

Characters and Unicode

Total characters308283
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowamsterdam
2nd rowamsterdam
3rd rowamsterdam
4th rowamsterdam
5th rowamsterdam

Common Values

ValueCountFrequency (%)
london 9993
19.3%
rome 9027
17.5%
paris 6688
12.9%
lisbon 5763
11.1%
athens 5280
10.2%
budapest 4022
7.8%
vienna 3537
 
6.8%
barcelona 2833
 
5.5%
berlin 2484
 
4.8%
amsterdam 2080
 
4.0%

Length

2023-08-12T15:50:53.211030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-12T15:50:53.413997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
london 9993
19.3%
rome 9027
17.5%
paris 6688
12.9%
lisbon 5763
11.1%
athens 5280
10.2%
budapest 4022
7.8%
vienna 3537
 
6.8%
barcelona 2833
 
5.5%
berlin 2484
 
4.8%
amsterdam 2080
 
4.0%

Most occurring characters

ValueCountFrequency (%)
n 43420
14.1%
o 37609
12.2%
a 29353
9.5%
e 29263
9.5%
s 23833
7.7%
r 23112
7.5%
l 21073
6.8%
i 18472
 
6.0%
d 16095
 
5.2%
b 15102
 
4.9%
Other values (7) 50951
16.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 308283
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 43420
14.1%
o 37609
12.2%
a 29353
9.5%
e 29263
9.5%
s 23833
7.7%
r 23112
7.5%
l 21073
6.8%
i 18472
 
6.0%
d 16095
 
5.2%
b 15102
 
4.9%
Other values (7) 50951
16.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 308283
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 43420
14.1%
o 37609
12.2%
a 29353
9.5%
e 29263
9.5%
s 23833
7.7%
r 23112
7.5%
l 21073
6.8%
i 18472
 
6.0%
d 16095
 
5.2%
b 15102
 
4.9%
Other values (7) 50951
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308283
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 43420
14.1%
o 37609
12.2%
a 29353
9.5%
e 29263
9.5%
s 23833
7.7%
r 23112
7.5%
l 21073
6.8%
i 18472
 
6.0%
d 16095
 
5.2%
b 15102
 
4.9%
Other values (7) 50951
16.5%

weekdays
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
False
26207 
True
25500 
ValueCountFrequency (%)
False 26207
50.7%
True 25500
49.3%
2023-08-12T15:50:53.615425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-08-12T15:50:41.615468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:15.423584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:17.811100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:20.187260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:22.738674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:25.232386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:27.472406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:29.843021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:32.053159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:34.510100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:36.859553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:39.309804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:41.843510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:15.628207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:18.011482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:20.398545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:22.933265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:25.421628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:27.668781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:30.030043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:32.486270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:34.713366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:37.051379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:39.503929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:42.055463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:15.834122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:18.218890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:20.606002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:23.128654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:25.618375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:27.882640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:30.224131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:32.666574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:34.922237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:37.266458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:39.703090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:42.262808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:16.037126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:18.423722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:20.817090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:23.350128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:25.816749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:28.089144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:30.418853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:32.866423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:35.131597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:37.496541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:39.912025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:42.449999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:16.225353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:18.609753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:21.009164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:23.551822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:25.989612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:28.275773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:30.597138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:33.041228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:35.339037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:37.717863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:40.092476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:42.635883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:16.406304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:18.801296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:21.360221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:23.748915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:26.162754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:28.472262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:30.770109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:33.218938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:35.522109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:37.986249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:40.273731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:42.834722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:16.618095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:19.010768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:21.560973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:23.968323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:26.356094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:28.684415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:30.953434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:33.422234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:35.718791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:38.192219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:40.465766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:43.226011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:16.798067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:19.190585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:21.742202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:24.178972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:26.527082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:28.865565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:31.121813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:33.590895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:35.897564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:38.371631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:40.645556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:43.414851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:16.999422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:19.383064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:21.933237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:24.373097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:26.705607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:29.049636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:31.297011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:33.765023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:36.082448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:38.560169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:40.827608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:43.608776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:17.195998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:19.586118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:22.133491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:24.588444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:26.903033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:29.255333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:31.479829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:33.956004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:36.276822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:38.752712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:41.023923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:43.794906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:17.402670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:19.778062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:22.326397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:24.841630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:27.083920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:29.446783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:31.653244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:34.132984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:36.462353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:38.927873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:41.204504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:43.989836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:17.605380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:19.989232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:22.525944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:25.032719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:27.278429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:29.642081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:31.845522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:34.323508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:36.654896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:39.119083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-12T15:50:41.399986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-12T15:50:53.765614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
realSumcleanliness_ratingguest_satisfaction_overallbedroomsdistmetro_distattr_indexattr_index_normrest_indexrest_index_normlnglatroom_typeroom_sharedroom_privateperson_capacityhost_is_superhostmultibizcityweekdays
realSum1.0000.0050.0140.226-0.061-0.1330.3850.4940.3330.310-0.3520.3570.0190.0000.0290.0340.0140.0080.0250.0270.000
cleanliness_rating0.0051.0000.6300.040-0.0240.041-0.072-0.112-0.080-0.0480.134-0.1000.0450.0430.0430.0100.3230.0480.1320.0670.000
guest_satisfaction_overall0.0140.6301.0000.0630.0270.055-0.114-0.096-0.135-0.0730.173-0.0240.0480.0330.0580.0160.3670.0610.2380.0740.000
bedrooms0.2260.0400.0631.000-0.0160.072-0.036-0.098-0.037-0.0590.018-0.1030.2280.0420.3170.3480.0280.0290.0160.0590.010
dist-0.061-0.0240.027-0.0161.0000.320-0.405-0.219-0.377-0.526-0.1990.4400.1530.0260.2140.0760.0620.0360.1570.2070.000
metro_dist-0.1330.0410.0550.0720.3201.000-0.224-0.268-0.222-0.324-0.0430.0090.0640.0190.0890.0210.0120.0300.0990.1140.000
attr_index0.385-0.072-0.114-0.036-0.405-0.2241.0000.7650.9350.662-0.2620.0440.0190.0170.0200.0200.0170.0170.0910.1460.000
attr_index_norm0.494-0.112-0.096-0.098-0.219-0.2680.7651.0000.6340.558-0.2710.5120.0260.0000.0370.0460.0950.0450.1020.2000.012
rest_index0.333-0.080-0.135-0.037-0.377-0.2220.9350.6341.0000.683-0.344-0.0170.0480.0280.0610.0380.0590.0080.1340.2230.000
rest_index_norm0.310-0.048-0.073-0.059-0.526-0.3240.6620.5580.6831.000-0.1080.0380.1180.0210.1650.0360.0240.0410.1010.2690.082
lng-0.3520.1340.1730.018-0.199-0.043-0.262-0.271-0.344-0.1081.000-0.2100.2320.0390.3240.1410.2170.0890.2181.0000.033
lat0.357-0.100-0.024-0.1030.4400.0090.0440.512-0.0170.038-0.2101.0000.2470.0440.3460.1230.1580.1200.1630.9930.027
room_type0.0190.0450.0480.2280.1530.0640.0190.0260.0480.1180.2320.2471.0001.0001.0000.3790.0580.1370.0580.2970.006
room_shared0.0000.0430.0330.0420.0260.0190.0170.0000.0280.0210.0390.0441.0001.0000.0630.0530.0270.0150.0440.0790.000
room_private0.0290.0430.0580.3170.2140.0890.0200.0370.0610.1650.3240.3461.0000.0631.0000.5340.0490.1370.0400.4110.007
person_capacity0.0340.0100.0160.3480.0760.0210.0200.0460.0380.0360.1410.1230.3790.0530.5341.0000.0360.0610.1120.1540.011
host_is_superhost0.0140.3230.3670.0280.0620.0120.0170.0950.0590.0240.2170.1580.0580.0270.0490.0361.0000.0970.1080.2200.004
multi0.0080.0480.0610.0290.0360.0300.0170.0450.0080.0410.0890.1200.1370.0150.1370.0610.0971.0000.4710.1230.002
biz0.0250.1320.2380.0160.1570.0990.0910.1020.1340.1010.2180.1630.0580.0440.0400.1120.1080.4711.0000.2300.017
city0.0270.0670.0740.0590.2070.1140.1460.2000.2230.2691.0000.9930.2970.0790.4110.1540.2200.1230.2301.0000.046
weekdays0.0000.0000.0000.0100.0000.0000.0000.0120.0000.0820.0330.0270.0060.0000.0070.0110.0040.0020.0170.0461.000

Missing values

2023-08-12T15:50:44.306698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-12T15:50:44.846369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

realSumroom_typeroom_sharedroom_privateperson_capacityhost_is_superhostmultibizcleanliness_ratingguest_satisfaction_overallbedroomsdistmetro_distattr_indexattr_index_normrest_indexrest_index_normlnglatcityweekdays
0194.033698Private roomFalseTrue2.0False1010.093.015.0229642.53938078.6903794.16670898.2538966.8464734.9056952.41772amsterdamTrue
1344.245776Private roomFalseTrue4.0False008.085.010.4883890.239404631.17637833.421209837.28075758.3429284.9000552.37432amsterdamTrue
2264.101422Private roomFalseTrue2.0False019.087.015.7483123.65162175.2758773.98590895.3869556.6467004.9751252.36103amsterdamTrue
3433.529398Private roomFalseTrue4.0False019.090.020.3848620.439876493.27253426.119108875.03309860.9735654.8941752.37663amsterdamTrue
4485.552926Private roomFalseTrue2.0True0010.098.010.5447380.318693552.83032429.272733815.30574056.8116774.9005152.37508amsterdamTrue
5552.808567Private roomFalseTrue3.0False008.0100.022.1314201.904668174.7889579.255191225.20166215.6923764.8769952.38966amsterdamTrue
6215.124317Private roomFalseTrue2.0False0010.094.011.8810920.729747200.16765210.599010242.76552416.9162514.9157052.38296amsterdamTrue
72771.307384Entire home/aptFalseFalse4.0True0010.0100.031.6868071.458404208.80810911.056528272.31382318.9752194.8846752.38749amsterdamTrue
81001.804420Entire home/aptFalseFalse4.0False009.096.023.7191411.196112106.2264565.624761133.8762029.3286864.8645952.40175amsterdamTrue
9276.521454Private roomFalseTrue2.0False1010.088.013.1423610.924404206.25286210.921226238.29125816.6044784.8760052.34700amsterdamTrue
realSumroom_typeroom_sharedroom_privateperson_capacityhost_is_superhostmultibizcleanliness_ratingguest_satisfaction_overallbedroomsdistmetro_distattr_indexattr_index_normrest_indexrest_index_normlnglatcityweekdays
51697289.988005Entire home/aptFalseFalse2.0False019.080.007.4132830.507324163.95447811.397115385.3272206.896686-0.2333951.49979londonFalse
51698442.625650Entire home/aptFalseFalse2.0False0110.080.014.1382450.573529447.16848731.084425708.75059412.685401-0.0679251.50893londonFalse
51699637.832498Entire home/aptFalseFalse2.0False017.040.014.4158480.388140329.95777322.936651740.13107013.247057-0.0653951.51684londonFalse
5170090.547755Private roomFalseTrue2.0False0010.0100.0112.2004143.080764103.2985887.180688207.5279743.7143890.0466151.49376londonFalse
51701362.191020Entire home/aptFalseFalse4.0False008.0100.028.5510821.168158131.6192949.149370304.9390175.457877-0.1881851.44146londonFalse
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